Inspired by constructing large‐size cointegrated portfolios, this paper considers a vector error correction model and develops the adaptive Lasso estimator of the cointegrating vectors. The asymptotic properties of the estimators and the oracle property of the adaptive Lasso are derived. An optimisation algorithm for estimating the model parameters is proposed. The simulation study shows the effectiveness of the parameter estimation procedures and the forecasting performance of our model. In the empirical study, we apply the proposed method to construct the sparse cointegrated portfolios with or without market‐neutral property. The trading performances of different types of cointegrated portfolios are evaluated using the Dow Jones Industrial Average composite stocks. The empirical findings reveal that the sparse cointegrated market‐neutral portfolios of a number of securities are capable to benefit the investors who wish to construct statistical arbitrage portfolios which are market‐neutral.

中文翻译：

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稀疏矢量误差校正模型及其在基于协整交易中的应用

受构建大型协整投资组合的启发，本文考虑了矢量误差校正模型，并开发了协整矢量的自适应套索估计器。推导了估计量的渐近性质和自适应套索的预言性质。提出了一种估计模型参数的优化算法。仿真研究表明了参数估计程序的有效性和模型的预测性能。在实证研究中，我们应用所提出的方法来构建具有或不具有市场中立属性的稀疏协整投资组合。使用道琼斯工业平均指数综合股票评估不同类型的协整投资组合的交易表现。